# -*- coding: utf-8 -*-
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# Copyright 2022 Huawei Technologies Co., Ltd
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import argparse
import time
from pathlib import Path

import cv2
import torch
if torch.__version__ >= '1.8':
    import torch_npu
import torch.backends.cudnn as cudnn
from numpy import random

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
import csv


def detect(save_img=False):
    source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
    trace = False
    save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))

    # Directories
    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Initialize
    set_logging()
    device = select_device(opt.device)
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_size

    if trace:
        model = TracedModel(model, device, opt.img_size)

    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    old_img_w = old_img_h = imgsz
    old_img_b = 1

    t0 = time.time()
    ly_result=[]
    existing_ids = set()
    for path, img, im0s, vid_cap in dataset:
        file_prefix = path.split('/')[-1].split('.')[0] # 得到文件前缀

        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Warmup
        if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
            old_img_b = img.shape[0]
            old_img_h = img.shape[2]
            old_img_w = img.shape[3]
            for i in range(3):
                model(img, augment=opt.augment)[0]

        # Inference
        t1 = time_synchronized()
        with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
            pred = model(img, augment=opt.augment)[0]
        t2 = time_synchronized()

        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t3 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        has_bottom=False #有没有瓶盖
        has_normal=False #有没有正常
        has_unnormal=False #有没有不正常
        wrong_flag=False #是否出错
        temp_list=[]
        len_pre_pix=0 #每个像素点的长度
        ratio=0
        #draw_box=True
        

        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # Write results
                #农夫山泉瓶盖的直径大约为32mm
                for *xyxy, conf, cls in reversed(det):
                    ##修改
                    x1,y1,x2,y2=xyxy
                    width=x2-x1
                    height=y2-y1
                    if(cls==1):#表示是瓶盖
                        if(has_bottom):
                            print("出现了不一致！")
                            print("旧的为：",wvsh)
                            boxmax=max(width,height)
                            boxmin=min(width,height)
                            new_wvsh=boxmax/boxmin
                            print("新的为：",new_wvsh)
                            if(new_wvsh<=wvsh):
                                #说明这才是一个真瓶盖
                                wvsh=new_wvsh
                                print("真的为：",wvsh)
                                len_pre_pix=32/((width+height)/2)
                                print("真的的len:",len_pre_pix)
                                
                        else:
                            has_bottom=True
                            boxmax=max(width,height)
                            boxmin=min(width,height)
                            wvsh=boxmax/boxmin
                            len_pre_pix=32/((width+height)/2)
                        
                        ratio=30/wvsh
                        
                    elif(cls==0):#表示正常
                        has_normal=True
                    elif(cls==2 or cls==3):#表示不正常
                        cls=0
                        has_unnormal=True
                    else:#什么也没有，出错了
                        wrong_flag=True

                    #储存得到的结果
                    # detect_res=[file_prefix,width,height,cls]
                    # temp_list.append(detect_res)
                    # print(f"类别：{cls},锚框高度: {height}, 置信度: {conf}, 锚框宽度: {width}")

                    # 准备检测结果
                    detect_res = [file_prefix, width, height, cls, conf]

                    # 检查是否存在相同类别的元素
                    existing = next((item for item in temp_list if item[3] == cls), None)
                    if existing:
                        # 比较置信度
                        if conf > existing[4]:
                            temp_list = [item for item in temp_list if item[3] != cls]
                            temp_list.append(detect_res)
                    else:
                        temp_list.append(detect_res)
                    print(f"类别：{cls}, 锚框高度: {height}, 置信度: {conf}, 锚框宽度: {width}")

                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or view_img:  # Add bbox to image
                        label = f'{names[int(cls)]} {conf:.2f}'
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

            # Print time (inference + NMS)
            print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                    print(f" The image with the result is saved in: {save_path}")
                else:  # 'video' or 'stream'
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                            save_path += '.mp4'
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer.write(im0)
            print(temp_list)
            for i in temp_list:
                if i[3]==0:
                    # 根据条件处理数据
                    print("处理的图像是:",i[0])
                    size=0
                    if has_normal and has_bottom:
                        #有红且有瓶盖，我们才进行判断，其余不判断，都认为是错误数据。
                        print("瓶盖：",len_pre_pix)
                        i[2] = i[2] * len_pre_pix
                        i[1] = i[1] * len_pre_pix
                        print("红肿的长和宽：",i[1],i[2])
                        size = (i[2] + i[1]) / 2
                        print("红肿区域大小为：")
                        print(size)
                        radius=max(i[1],i[2])*ratio
                        if size.item() > 5:
                            i[3] = '1'
                        else:
                            i[3] = '0'
                        if radius>5:
                            re='1'
                        else:
                            re='0'
                        
                        if i[0] not in existing_ids:
                            ly_result.append([i[0],i[3],re])
                            existing_ids.add(i[0])
                    # if has_normal and has_bottom==False:
                    #     #有红但是没有瓶盖
                    #     #那我们就不用管啦
                    # elif has_unnormal:
                    #     i[3] = '1'
                    # elif has_bottom and has_normal==False:
                    #     #有瓶盖但是没有红
                    #     i[3]='0'
                    
                # elif i[3]==1:
                #     if has_bottom and has_normal==False:
                #         #有瓶盖但是没有红
                #         #不用管啦
                #         print("问题数据")
                    
                    

    import pandas as pd
    result = pd.DataFrame(ly_result,columns=['id','size_result','radius_result'])
    result.to_csv('./recheck_1.csv')


    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        #print(f"Results saved to {save_dir}{s}")

    print(f'Done. ({time.time() - t0:.3f}s)')


if __name__ == '__main__':
    torch.npu.set_compile_mode(jit_compile=False)
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default='runs/train/improved_2_result/weights/last.pt', help='model.pt path(s)')
    parser.add_argument('--source', type=str, default='VOC/all/images', help='source')  # file/folder, 0 for webcam
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.2, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.4, help='IOU threshold for NMS')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--no-trace', action='store_true', help='don`t trace model')
    opt = parser.parse_args()
    print(opt)
    #check_requirements(exclude=('pycocotools', 'thop'))

    with torch.no_grad():
        if opt.update:  # update all models (to fix SourceChangeWarning)
            for opt.weights in ['yolov7.pt']:
                detect()
                strip_optimizer(opt.weights)
        else:
            detect()
